The objective of this project is to predict house prices using machine learning techniques. To achieve this, I performed a brief exploratory data analysis and directed the study to evaluate the Boruta feature selection technique, comparing its performance with the neural network both with and without the use of this technique.
The dataset used contains house sale prices for King County, which includes Seattle, and it includes homes sold between May 2014 and May 2015.
Dataset source: Kaggle
- Tensorflow
- Pandas
- Numpy
- Matplotlib
- Boruta (for feature selection)
- Gradio - user interface (ux)
The following metrics were used to assess the performance of the models:
- RMSE: Root Mean Squared Error
- MSE: Mean Squared Error
- MAE: Mean Absolute Error
- R2: The determination coefficient
- Adjusted R2
Below are some characteristics of the house as input for the application to return the forecast of the house's value.
Below is a list of the features used:
- sqft_living: Living area square footage.
- -sqft_lot: Lot size square footage.
- waterfront: Indicates if the property has a waterfront view (1) or not (0).
- view: Number of times the property has been viewed.
- grade: Overall grade given to the housing unit, based on the King County grading system.
- sqft_above: Square footage of the home's interior living space above ground level.
- yr_built: Year the house was built.
- zipcode: Zip code of the area where the property is located.
- lat: Latitude of the property.
- long: Longitude of the property.
- sqft_living15: Average living area square footage of the nearest 15 houses.
- sqft_lot15: Average lot size square footage of the nearest 15 houses.
The results of this project indicate that the model that uses all resources performed better than the model that used the Boruta feature selection technique. However, when choosing the best model for a specific application, it is important to consider not only the performance but also the computational cost and the efficiency in the selection of resources. For future versions of this model, it would be interesting to explore other feature selection and feature engineering techniques.
This project is licensed under the MIT License.